• Why data viz?
• Types of viz
• Best practices
• What to watch for when reading viz or avoid when creating viz

Communicating data

• Raw data
• Summaries of raw data, reports
• Results of predictive models

Data analysts use it in every step of the data analysis workflow:

• Data is the foundation, but is often messy, so use viz while collecting, organizing, merging, checking etc., data sets. Identify outliers and potential inaccurate data
• Assess the results of models
• Checking your work every step
• etc

• Tables of numbers can be used for those similar purposes, often effectively. Viz can often convey the most important information more quickly and easily

• Often relative relationships are more important than exact numbers

  • If exact numbers are important, they can go in a table or can sometimes be shown in viz (e.g. geom_text)

• Some relationships are difficult to express in a table

  • Spatial relationships (location)
  • Temporal relationships (time)
  • Spatial temporal relationships (location and time)

• Table with two columns
  • zip code
  • number of customers (or revenue, or tickets, etc.)
• Lose spatial information
  • What zip codes are adjacent?
  • Neighborhoods, highways, landmarks
  • Table is not as intuitive

       date new_cases
 2020-09-01     41667
 2020-09-02     40940
 2020-09-03     44085
 2020-09-04     50415
 2020-09-05     42982
 2020-09-06     31299
 2020-09-07     23468
 2020-09-08     27435
 2020-09-09     33754
 2020-09-10     36120
 2020-09-11     47649
 2020-09-12     41077

No obvious trends.

       date away home attendance
 2017-04-02  SFN  ARI      49016
 2017-04-02  CHN  SLN      47566
 2017-04-02  NYA  TBA      31042
 2017-04-03  PHI  CIN      43804
 2017-04-03  SDN  LAN      53701
 2017-04-03  COL  MIL      43336
 2017-04-03  ATL  NYN      44384
 2017-04-03  MIA  WAS      42744
 2017-04-03  TOR  BAL      45667
 2017-04-03  PIT  BOS      36594
 2017-04-03  SEA  HOU      41678
 2017-04-03  KCA  MIN      39615

No obvious weekly trends.

• Bar
• Line
• Scatter
• Histogram
• Heat map

• Or “bar graph”, “bar plot”
• Categorical data
• Bar for each category, length of bar according to some value
• Example: Alcohol use by age
  • Age ranges - categorical
  • Percentage of those in range who used in the past 12 months - value used as length of the bars

• Or “line graph”, “line plot”
• Often used to display trends in numeric data over time
• Example: New COVID Cases By Date

• If there aren’t many data points, can show points too.
• Example: Player rating over time
  • Year - the measure of time
  • Player rating - numeric data we want to the trend for

• Display the relationship between two numeric variables in a data set
• Example: Cost vs Interventions for heart disease patients
  • Interventions - numeric
  • Total cost - also numeric

• Size or color dots based on other information

• Numeric data (unlike categorical data for bar plot)
• Shows ranges of values, how often they occur
• Different than bar chart (which was for categorical data)

Use color to show magnitude of numeric data over 2 dimensions

• Ensure viz answers a question, highlights an observation, serves some academic or business purpose
• Simple, to the point.
  • Use types of charts people are familiar with and can understand easily, highlight main takeaways
  • Avoid distracting colors and unnecessary features from the main takeaways
  • Avoid TMI.
• Use color wisely
  • draw attention to main takeaway(s)
  • separate context/background data and the data of interest
  • indicate different categories

• Label well
  • Title - always
  • Axes - very often
  • Legend - when applicable
• Give context
  • What is average?
  • What is a normal range?
  • What was it last year?
• Show uncertainty when possible/appropriate

Viz with no clear takeaway

• Is the chart showing relevant and appropriate information?
• Does it answer a question appropriately?
• Is the metric appropriate? Should it be a rate or percentage instead of a count?

• Axes that don’t start at zero
  • This can be the default in some software packages
  • Can give misleading differences in bar lengths
  • Ex: Treatment vs placebo comparison

• Broken axes
  • Misleading differences in bars, for example
• Scale of axes
  • What is the range of values? Very small or large?
  • Is log scale being used?

• If a result looks weird, could it be a sample size issue?
• Different sized bins, different sized groups when binned
  • Ex. Drug use by age

• Line chart with categorical data on horizontal axes
  • Bar chart is probably better
• Pie charts
  • Use for proportions.
  • Even with proportions, can be tough to compare categories
  • Can you use a bar chart?

• Radar charts
  • Areas can be misleading, have no meaning
  • Difficult to compare categories
  • Can you use a bar chart?

• 3D bar chart
  • Unnecessary third dimension can yield misleading bar heights
  • Adds clutter/distraction, provides no information
  • Can you use a (2D) bar chart?
    
• Stacked bars
  • In some cases, appropriate
  • In some cases, tough to compare
  • Can you use separate bar charts?

• Too many colors, difficult to distinguish.
• Colors that are not colorblind-friendly
  • Choose a different palette, or
  • Use different shapes (e.g. circles, squares, diamonds) or lines (e.g. solid, dotted, dashed) to further distinguish

Note: 5% of the population (mostly male) is Red-Green colorblind

• Avoid red and green together, without other ways to distinguish.
• Green/Yellow/Red is a frequently-used color palette.

Want the option of more information, but don’t want to clutter the viz. 

Show information on hover

Want to show trends in data over time, but line plot is too simple.

Example: Player and ball locations over time, 25 times a second. Impossible to process by looking at the data set.

• What is the purpose?

  • What is the question you are trying to answer?

• Who is the audience?

  • Is your visualization appropriate for this audience?

• In what context will they see this visualization?

• What is the key observation or takeaway?

  • Is that obvious from a quick look at the visualization?
  • Can color or labeling be used to highlight the main takeaway?

• Does the visualization have unnecessary features that distract from the main takeaway?